Author: Dann Corbit
Date: 13:20:49 01/17/06
Go up one level in this thread
On January 17, 2006 at 05:41:22, Joseph Ciarrochi wrote: >Another issue is that players may vary in how variable their play is, and more >variable players would have larger standard errors for a given n size > >I used a stat package to generate the following statistical output. player 1 and >2 have the same mean level of points and the same N, but player 1 tends to win >and lose alot, wherease player 2 tends to draw alot. AS you can see, standard >error is bigger for player 1. > >This sort of analysis assumes that the variable "mean" is contionous, though >really we have three categories(win, draw, loss). Still, with large samples, it >is probably ok. I guess I could just use the formula for confidence intervals >around means. > >The formula you suggest is useful in that it does give you the confidence >interval for win verus everything else, which is an important statistic > > > >PLAYER Mean N Std. Deviation Std. Error of Mean >1.00 .5000 300 .48385 .02794 >2.00 .5000 300 .40893 .02361 >Total .5000 600 .44759 .01827 I think that the following study would be interesting (it will require that at least 30 people have chosen the move): Figure out if a move really improves on expectancy. It sounds simple, but it must also take into account the Elo of the player, whether they are playing black or white, and the Elo of the opponent. We must also take the entire game into account. My notion is to classify openings [or parts of openings] as underperforming, average performing, or overperforming. In other words, a player with a higher Elo and who is also playing white has TWO advantages. We would expect that a certain percentage of the time they should win against someone with a lower Elo or someone with an equal Elo playing black. And then, we may have a slightly higher Elo playing black. So who "should" have the advantage becomes complicated. After working out the initial theory, we could make some measurements with actual data and see how well the prediction fits the model for the entire database. Once the model has been corrected to give good results, we could then figure out how good moves "turn out" as a function of how they "ought to turn out" and the strength of the players and other advantages they may have.
This page took 0 seconds to execute
Last modified: Thu, 15 Apr 21 08:11:13 -0700
Current Computer Chess Club Forums at Talkchess. This site by Sean Mintz.